Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 951–975 of 1746 papers

Title	Date	Tasks	Status	Hype
Rethinking Classifier and Adversarial Attack	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer	May 2, 2022	Adversarial Robustnessimage-classification	—Unverified	0
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning	May 1, 2022	Adversarial Robustnesstext-classification	CodeCode Available	1
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples	May 1, 2022	Adversarial Robustness	—Unverified	0
Engineering flexible machine learning systems by traversing functionally-invariant paths	Apr 30, 2022	Adversarial RobustnessContinual Learning	CodeCode Available	1
Adversarial Fine-tune with Dynamically Regulated Adversary	Apr 28, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
On Fragile Features and Batch Normalization in Adversarial Training	Apr 26, 2022	Adversarial Robustness	—Unverified	0
Testing robustness of predictions of trained classifiers against naturally occurring perturbations	Apr 21, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning	Apr 15, 2022	Adversarial RobustnessAutonomous Driving	—Unverified	0
Planting Undetectable Backdoors in Machine Learning Models	Apr 14, 2022	Adversarial RobustnessBIG-bench Machine Learning	—Unverified	0
Q-TART: Quickly Training for Adversarial Robustness and in-Transferability	Apr 14, 2022	Adversarial Robustness	—Unverified	0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks	Apr 14, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
A Simple Approach to Adversarial Robustness in Few-shot Image Classification	Apr 11, 2022	Adversarial RobustnessFew-Shot Image Classification	CodeCode Available	0
Evaluating the Adversarial Robustness for Fourier Neural Operators	Apr 8, 2022	Adversarial Robustnessscientific discovery	—Unverified	0
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network	Apr 6, 2022	Adversarial RobustnessModel Compression	CodeCode Available	1
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck	Apr 6, 2022	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness through the Lens of Convolutional Filters	Apr 5, 2022	Adversarial Robustness	CodeCode Available	0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning	Apr 5, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning	Apr 2, 2022	Adversarial RobustnessAutonomous Driving	CodeCode Available	0
Scalable Whitebox Attacks on Tree-based Models	Mar 31, 2022	Adversarial Robustness	—Unverified	0
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective	Mar 27, 2022	Adversarial Robustnessimage-classification	CodeCode Available	1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow	Mar 24, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes	Mar 22, 2022	Adversarial Robustness	—Unverified	0
Robustness through Cognitive Dissociation Mitigation in Contrastive Adversarial Training	Mar 16, 2022	Adversarial RobustnessClassification	CodeCode Available	0
Provable Adversarial Robustness for Fractional Lp Threat Models	Mar 16, 2022	Adversarial Robustness	CodeCode Available	0

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All datasets AdvGLUE CIFAR-10 ImageNet ImageNet-A ImageNet-C Stylized ImageNet CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeBERTa (single model)	Accuracy	0.61	—	Unverified
2	ALBERT (single model)	Accuracy	0.59	—	Unverified
3	T5 (single model)	Accuracy	0.57	—	Unverified
4	SMART_RoBERTa (single model)	Accuracy	0.54	—	Unverified
5	FreeLB (single model)	Accuracy	0.5	—	Unverified
6	RoBERTa (single model)	Accuracy	0.5	—	Unverified
7	InfoBERT (single model)	Accuracy	0.46	—	Unverified
8	ELECTRA (single model)	Accuracy	0.42	—	Unverified
9	BERT (single model)	Accuracy	0.34	—	Unverified
10	SMART_BERT (single model)	Accuracy	0.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed classifier	Accuracy	95.23	—	Unverified
2	Stochastic-LWTA/PGD/WideResNet-34-10	Accuracy	92.26	—	Unverified
3	Stochastic-LWTA/PGD/WideResNet-34-5	Accuracy	91.88	—	Unverified
4	GLOT-DR	Accuracy	84.13	—	Unverified
5	TRADES-ANCRA/ResNet18	Accuracy	81.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (SGD, Cosine)	Accuracy	77.4	—	Unverified
2	ResNet-50 (SGD, Step)	Accuracy	76.9	—	Unverified
3	DeiT-S (AdamW, Cosine)	Accuracy	76.8	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	76.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	12.2	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	3.3	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	3.2	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	3.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (AdamW, Cosine)	mean Corruption Error (mCE)	59.3	—	Unverified
2	ResNet-50 (SGD, Step)	mean Corruption Error (mCE)	57.9	—	Unverified
3	ResNet-50 (SGD, Cosine)	mean Corruption Error (mCE)	56.9	—	Unverified
4	DeiT-S (AdamW, Cosine)	mean Corruption Error (mCE)	48	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	13	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	8.4	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	8.3	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	8.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed Classifier	Clean Accuracy	85.21	—	Unverified
2	ResNet18/MART-ANCRA	Clean Accuracy	60.1	—	Unverified